Overview

Dataset statistics

Number of variables16
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.6 KiB
Average record size in memory129.3 B

Variable types

Numeric8
Categorical2
Unsupported6

Warnings

Country has a high cardinality: 100 distinct values High cardinality
New Cases is highly correlated with New Deaths and 1 other fieldsHigh correlation
New Deaths is highly correlated with New Cases and 1 other fieldsHigh correlation
New Recovered is highly correlated with New Cases and 1 other fieldsHigh correlation
df_index is highly correlated with New Cases and 2 other fieldsHigh correlation
New Cases is highly correlated with df_index and 2 other fieldsHigh correlation
New Deaths is highly correlated with df_index and 2 other fieldsHigh correlation
New Recovered is highly correlated with df_index and 2 other fieldsHigh correlation
df_index is highly correlated with New Cases and 2 other fieldsHigh correlation
New Cases is highly correlated with df_index and 2 other fieldsHigh correlation
New Deaths is highly correlated with df_index and 2 other fieldsHigh correlation
New Recovered is highly correlated with df_index and 2 other fieldsHigh correlation
Active Cases is highly correlated with Continent and 6 other fieldsHigh correlation
df_index is highly correlated with CountryHigh correlation
Continent is highly correlated with Active Cases and 7 other fieldsHigh correlation
Country is highly correlated with Active Cases and 8 other fieldsHigh correlation
New Deaths is highly correlated with Active Cases and 6 other fieldsHigh correlation
New Cases is highly correlated with Active Cases and 6 other fieldsHigh correlation
Total Recovered is highly correlated with Active Cases and 6 other fieldsHigh correlation
Total Cases/1M is highly correlated with Continent and 1 other fieldsHigh correlation
Total Cases is highly correlated with Active Cases and 6 other fieldsHigh correlation
New Recovered is highly correlated with Active Cases and 6 other fieldsHigh correlation
Continent is highly correlated with CountryHigh correlation
Country is highly correlated with ContinentHigh correlation
Country is uniformly distributed Uniform
df_index has unique values Unique
Country has unique values Unique
Total Cases has unique values Unique
Total Recovered has unique values Unique
Total Cases/1M has unique values Unique
Total Deaths is an unsupported type, check if it needs cleaning or further analysis Unsupported
Serious/Critical is an unsupported type, check if it needs cleaning or further analysis Unsupported
Deaths/1M is an unsupported type, check if it needs cleaning or further analysis Unsupported
Total Tests is an unsupported type, check if it needs cleaning or further analysis Unsupported
Test/1M is an unsupported type, check if it needs cleaning or further analysis Unsupported
Population is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2021-07-22 13:14:08.951251
Analysis finished2021-07-22 13:20:57.130528
Duration6 minutes and 48.18 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.15
Minimum5
Maximum211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-07-22T18:50:57.634482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile22.45
Q160.75
median119.5
Q3172.5
95-th percentile203.05
Maximum211
Range206
Interquartile range (IQR)111.75

Descriptive statistics

Standard deviation60.93204942
Coefficient of variation (CV)0.5291537075
Kurtosis-1.236516201
Mean115.15
Median Absolute Deviation (MAD)56
Skewness-0.08298493528
Sum11515
Variance3712.714646
MonotonicityNot monotonic
2021-07-22T18:50:58.138436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2111
 
1.0%
851
 
1.0%
631
 
1.0%
641
 
1.0%
661
 
1.0%
681
 
1.0%
711
 
1.0%
771
 
1.0%
781
 
1.0%
801
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
51
1.0%
61
1.0%
91
1.0%
101
1.0%
121
1.0%
231
1.0%
241
1.0%
261
1.0%
291
1.0%
301
1.0%
ValueCountFrequency (%)
2111
1.0%
2101
1.0%
2081
1.0%
2051
1.0%
2041
1.0%
2031
1.0%
2021
1.0%
2001
1.0%
1991
1.0%
1981
1.0%

Country
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
Zambia
 
1
Western Sahara
 
1
French Guiana
 
1
Belarus
 
1
Bulgaria
 
1
Other values (95)
95 

Length

Max length22
Median length7.5
Mean length8.82
Min length3

Characters and Unicode

Total characters882
Distinct characters52
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowPhilippines
2nd rowGuinea-Bissau
3rd rowGuinea
4th rowGhana
5th rowTrinidad and Tobago

Common Values

ValueCountFrequency (%)
Zambia1
 
1.0%
Western Sahara1
 
1.0%
French Guiana1
 
1.0%
Belarus1
 
1.0%
Bulgaria1
 
1.0%
Panama1
 
1.0%
Gabon1
 
1.0%
Morocco1
 
1.0%
Total:1
 
1.0%
Guinea-Bissau1
 
1.0%
Other values (90)90
90.0%

Length

2021-07-22T18:50:59.234337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and5
 
3.8%
islands4
 
3.0%
new3
 
2.3%
saint2
 
1.5%
guinea2
 
1.5%
french2
 
1.5%
gabon1
 
0.8%
dominican1
 
0.8%
ireland1
 
0.8%
hungary1
 
0.8%
Other values (111)111
83.5%

Most occurring characters

ValueCountFrequency (%)
a146
16.6%
i81
 
9.2%
n69
 
7.8%
e53
 
6.0%
r49
 
5.6%
o45
 
5.1%
s38
 
4.3%
34
 
3.9%
t34
 
3.9%
l30
 
3.4%
Other values (42)303
34.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter711
80.6%
Uppercase Letter134
 
15.2%
Space Separator34
 
3.9%
Other Punctuation2
 
0.2%
Dash Punctuation1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a146
20.5%
i81
11.4%
n69
9.7%
e53
 
7.5%
r49
 
6.9%
o45
 
6.3%
s38
 
5.3%
t34
 
4.8%
l30
 
4.2%
u30
 
4.2%
Other values (16)136
19.1%
Uppercase Letter
ValueCountFrequency (%)
C14
 
10.4%
B13
 
9.7%
M13
 
9.7%
S12
 
9.0%
G10
 
7.5%
A9
 
6.7%
P6
 
4.5%
T6
 
4.5%
I6
 
4.5%
N6
 
4.5%
Other values (12)39
29.1%
Other Punctuation
ValueCountFrequency (%)
:1
50.0%
.1
50.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%
Space Separator
ValueCountFrequency (%)
34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin845
95.8%
Common37
 
4.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a146
17.3%
i81
 
9.6%
n69
 
8.2%
e53
 
6.3%
r49
 
5.8%
o45
 
5.3%
s38
 
4.5%
t34
 
4.0%
l30
 
3.6%
u30
 
3.6%
Other values (38)270
32.0%
Common
ValueCountFrequency (%)
34
91.9%
-1
 
2.7%
:1
 
2.7%
.1
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII881
99.9%
Latin 1 Sup1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a146
16.6%
i81
 
9.2%
n69
 
7.8%
e53
 
6.0%
r49
 
5.6%
o45
 
5.1%
s38
 
4.3%
34
 
3.9%
t34
 
3.9%
l30
 
3.4%
Other values (41)302
34.3%
Latin 1 Sup
ValueCountFrequency (%)
ç1
100.0%

Total Cases
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2382947.66
Minimum10
Maximum192795147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-07-22T18:50:59.762289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile303.85
Q16253.25
median46239.5
Q3382756
95-th percentile4242736.55
Maximum192795147
Range192795137
Interquartile range (IQR)376502.75

Descriptive statistics

Standard deviation19267189.56
Coefficient of variation (CV)8.085443875
Kurtosis99.28212616
Mean2382947.66
Median Absolute Deviation (MAD)45982
Skewness9.947439574
Sum238294766
Variance3.712245936 × 1014
MonotonicityNot monotonic
2021-07-22T18:51:00.394231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30701
 
1.0%
5663561
 
1.0%
814671
 
1.0%
1383001
 
1.0%
425581
 
1.0%
59116011
 
1.0%
2957461
 
1.0%
253091
 
1.0%
55941
 
1.0%
64731
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
101
1.0%
561
1.0%
1311
1.0%
1641
1.0%
2061
1.0%
3091
1.0%
5571
1.0%
6271
1.0%
9491
1.0%
10051
1.0%
ValueCountFrequency (%)
1927951471
1.0%
60302401
1.0%
59116011
1.0%
47988511
1.0%
46799941
1.0%
42197231
1.0%
16028541
1.0%
15244381
1.0%
14247151
1.0%
10963411
1.0%

New Cases
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7114.43
Minimum-1
Maximum556432
Zeros0
Zeros (%)0.0%
Negative26
Negative (%)26.0%
Memory size928.0 B
2021-07-22T18:51:00.914185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median95.5
Q3798.5
95-th percentile13083.5
Maximum556432
Range556433
Interquartile range (IQR)799.5

Descriptive statistics

Standard deviation55698.03846
Coefficient of variation (CV)7.828882772
Kurtosis98.44422689
Mean7114.43
Median Absolute Deviation (MAD)96.5
Skewness9.8870993
Sum711443
Variance3102271488
MonotonicityNot monotonic
2021-07-22T18:51:01.418140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-126
26.0%
42
 
2.0%
322
 
2.0%
12
 
2.0%
232
 
2.0%
215391
 
1.0%
791
 
1.0%
261
 
1.0%
11421
 
1.0%
4311
 
1.0%
Other values (61)61
61.0%
ValueCountFrequency (%)
-126
26.0%
12
 
2.0%
21
 
1.0%
42
 
2.0%
51
 
1.0%
61
 
1.0%
71
 
1.0%
81
 
1.0%
91
 
1.0%
101
 
1.0%
ValueCountFrequency (%)
5564321
1.0%
305871
1.0%
237041
1.0%
215391
1.0%
146321
1.0%
130021
1.0%
112441
1.0%
65491
1.0%
39401
1.0%
27051
1.0%

Total Deaths
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size928.0 B

New Deaths
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct24
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.89
Minimum-1
Maximum8640
Zeros0
Zeros (%)0.0%
Negative55
Negative (%)55.0%
Memory size928.0 B
2021-07-22T18:51:01.970089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q34.75
95-th percentile64.3
Maximum8640
Range8641
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation867.1378893
Coefficient of variation (CV)8.037240609
Kurtosis97.50289946
Mean107.89
Median Absolute Deviation (MAD)0
Skewness9.822419596
Sum10789
Variance751928.1191
MonotonicityNot monotonic
2021-07-22T18:51:02.522039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
-155
55.0%
17
 
7.0%
36
 
6.0%
25
 
5.0%
322
 
2.0%
262
 
2.0%
112
 
2.0%
182
 
2.0%
122
 
2.0%
42
 
2.0%
Other values (14)15
 
15.0%
ValueCountFrequency (%)
-155
55.0%
17
 
7.0%
25
 
5.0%
36
 
6.0%
42
 
2.0%
71
 
1.0%
92
 
2.0%
101
 
1.0%
112
 
2.0%
122
 
2.0%
ValueCountFrequency (%)
86401
1.0%
7831
1.0%
4371
1.0%
3511
1.0%
1081
1.0%
621
1.0%
521
1.0%
421
1.0%
401
1.0%
322
2.0%

Total Recovered
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2175618.94
Minimum8
Maximum175323677
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-07-22T18:51:03.065991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile261.5
Q14950.75
median38202
Q3328015.25
95-th percentile3715936.75
Maximum175323677
Range175323669
Interquartile range (IQR)323064.5

Descriptive statistics

Standard deviation17521399.74
Coefficient of variation (CV)8.053524178
Kurtosis99.2561387
Mean2175618.94
Median Absolute Deviation (MAD)38024
Skewness9.945546776
Sum217561894
Variance3.069994487 × 1014
MonotonicityNot monotonic
2021-07-22T18:51:03.577944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44318711
 
1.0%
2932081
 
1.0%
131221
 
1.0%
10734751
 
1.0%
1754291
 
1.0%
4076221
 
1.0%
3044561
 
1.0%
5881
 
1.0%
302661
 
1.0%
139071
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
81
1.0%
531
1.0%
581
1.0%
1611
1.0%
1951
1.0%
2651
1.0%
4621
1.0%
5141
1.0%
5881
1.0%
6181
1.0%
ValueCountFrequency (%)
1753236771
1.0%
56664401
1.0%
54047971
1.0%
44355501
1.0%
44318711
1.0%
36782561
1.0%
15571991
1.0%
14495561
1.0%
13934661
1.0%
10734751
1.0%

New Recovered
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct62
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4882.02
Minimum-1
Maximum397019
Zeros0
Zeros (%)0.0%
Negative34
Negative (%)34.0%
Memory size928.0 B
2021-07-22T18:51:04.105897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median45
Q3658.5
95-th percentile5486.35
Maximum397019
Range397020
Interquartile range (IQR)659.5

Descriptive statistics

Standard deviation39717.45157
Coefficient of variation (CV)8.135454499
Kurtosis98.88731471
Mean4882.02
Median Absolute Deviation (MAD)46
Skewness9.919426309
Sum488202
Variance1577475959
MonotonicityNot monotonic
2021-07-22T18:51:04.713841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-134
34.0%
12
 
2.0%
642
 
2.0%
452
 
2.0%
82
 
2.0%
42
 
2.0%
108761
 
1.0%
111
 
1.0%
1571
 
1.0%
10241
 
1.0%
Other values (52)52
52.0%
ValueCountFrequency (%)
-134
34.0%
12
 
2.0%
42
 
2.0%
82
 
2.0%
111
 
1.0%
121
 
1.0%
141
 
1.0%
161
 
1.0%
191
 
1.0%
201
 
1.0%
ValueCountFrequency (%)
3970191
1.0%
225841
1.0%
126841
1.0%
108761
1.0%
82481
1.0%
53411
1.0%
26311
1.0%
21961
1.0%
19331
1.0%
19051
1.0%

Active Cases
Real number (ℝ≥0)

HIGH CORRELATION

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156366.43
Minimum1
Maximum13329448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-07-22T18:51:05.337784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q1258.75
median1964.5
Q313555.25
95-th percentile131520.8
Maximum13329448
Range13329447
Interquartile range (IQR)13296.5

Descriptive statistics

Standard deviation1332608.558
Coefficient of variation (CV)8.522344326
Kurtosis99.38438547
Mean156366.43
Median Absolute Deviation (MAD)1947.5
Skewness9.955096551
Sum15636643
Variance1.775845568 × 1012
MonotonicityNot monotonic
2021-07-22T18:51:06.025722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73
 
3.0%
562
 
2.0%
203921
 
1.0%
31021
 
1.0%
1591
 
1.0%
19131
 
1.0%
39981
 
1.0%
2901
 
1.0%
250781
 
1.0%
5371
 
1.0%
Other values (87)87
87.0%
ValueCountFrequency (%)
11
 
1.0%
21
 
1.0%
31
 
1.0%
73
3.0%
81
 
1.0%
111
 
1.0%
161
 
1.0%
181
 
1.0%
271
 
1.0%
361
 
1.0%
ValueCountFrequency (%)
133294481
1.0%
4747381
1.0%
4603011
1.0%
2641621
1.0%
1336071
1.0%
1314111
1.0%
1269621
1.0%
710111
1.0%
515291
1.0%
515211
1.0%

Serious/Critical
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size928.0 B

Total Cases/1M
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38220.528
Minimum16
Maximum149944
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-07-22T18:51:06.641666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile522.3
Q12698.75
median21176
Q371913.75
95-th percentile101610.05
Maximum149944
Range149928
Interquartile range (IQR)69215

Descriptive statistics

Standard deviation38228.98631
Coefficient of variation (CV)1.000221303
Kurtosis-0.7044833684
Mean38220.528
Median Absolute Deviation (MAD)20495.5
Skewness0.6772213828
Sum3822052.8
Variance1461455394
MonotonicityNot monotonic
2021-07-22T18:51:07.177617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
374041
 
1.0%
14971
 
1.0%
149511
 
1.0%
802761
 
1.0%
902151
 
1.0%
566361
 
1.0%
110971
 
1.0%
23141
 
1.0%
24491
 
1.0%
28541
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
161
1.0%
851
1.0%
2231
1.0%
4541
1.0%
5091
1.0%
5231
1.0%
5651
1.0%
6301
1.0%
6621
1.0%
6991
1.0%
ValueCountFrequency (%)
1499441
1.0%
1242761
1.0%
1088841
1.0%
1078491
1.0%
1051641
1.0%
1014231
1.0%
994811
1.0%
988071
1.0%
970341
1.0%
954561
1.0%

Deaths/1M
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size928.0 B

Total Tests
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size928.0 B

Test/1M
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size928.0 B

Population
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size928.0 B

Continent
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
Africa
29 
Europe
25 
North America
20 
Asia
13 
South America
Other values (2)

Length

Max length17
Median length6
Mean length8.15
Min length3

Characters and Unicode

Total characters815
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st rowAsia
2nd rowAfrica
3rd rowAfrica
4th rowAfrica
5th rowNorth America

Common Values

ValueCountFrequency (%)
Africa29
29.0%
Europe25
25.0%
North America20
20.0%
Asia13
13.0%
South America7
 
7.0%
Australia/Oceania5
 
5.0%
All1
 
1.0%

Length

2021-07-22T18:51:09.289426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-22T18:51:09.665392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
africa29
22.8%
america27
21.3%
europe25
19.7%
north20
15.7%
asia13
10.2%
south7
 
5.5%
australia/oceania5
 
3.9%
all1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
r106
13.0%
a89
10.9%
i79
 
9.7%
A75
 
9.2%
c61
 
7.5%
e57
 
7.0%
o52
 
6.4%
u37
 
4.5%
t32
 
3.9%
f29
 
3.6%
Other values (12)198
24.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter651
79.9%
Uppercase Letter132
 
16.2%
Space Separator27
 
3.3%
Other Punctuation5
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r106
16.3%
a89
13.7%
i79
12.1%
c61
9.4%
e57
8.8%
o52
8.0%
u37
 
5.7%
t32
 
4.9%
f29
 
4.5%
h27
 
4.1%
Other values (5)82
12.6%
Uppercase Letter
ValueCountFrequency (%)
A75
56.8%
E25
 
18.9%
N20
 
15.2%
S7
 
5.3%
O5
 
3.8%
Space Separator
ValueCountFrequency (%)
27
100.0%
Other Punctuation
ValueCountFrequency (%)
/5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin783
96.1%
Common32
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
r106
13.5%
a89
11.4%
i79
10.1%
A75
9.6%
c61
 
7.8%
e57
 
7.3%
o52
 
6.6%
u37
 
4.7%
t32
 
4.1%
f29
 
3.7%
Other values (10)166
21.2%
Common
ValueCountFrequency (%)
27
84.4%
/5
 
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r106
13.0%
a89
10.9%
i79
 
9.7%
A75
 
9.2%
c61
 
7.5%
e57
 
7.0%
o52
 
6.4%
u37
 
4.5%
t32
 
3.9%
f29
 
3.6%
Other values (12)198
24.3%

Interactions

2021-07-22T18:44:30.741275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:44:31.397216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:44:35.687252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:44:36.135211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:44:36.623166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:44:40.678799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:44:41.578489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:44:45.180635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:44:48.142250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:44:57.188224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:45:08.866524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:45:21.017421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:45:28.202568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:45:39.130629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:45:52.082616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:46:07.971483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:46:21.725894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:46:22.128140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:46:26.267402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:46:26.711658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:46:27.167288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:46:30.765493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:46:31.235522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:46:34.251215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:46:38.122471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:46:38.474440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:46:41.702309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:46:42.158267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:46:42.550231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:46:46.189903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:46:46.637860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:46:49.829571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:46:53.521672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:47:02.694890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:47:13.426974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:47:22.024945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:47:30.478389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:47:43.261559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:47:51.309965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:48:01.820382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:48:13.095181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:48:13.602602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:48:17.028372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:48:17.500572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:48:18.010273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:48:21.576020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:48:22.072365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:48:25.491492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:48:29.147337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:48:38.018708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:48:49.220588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:48:57.580082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:49:04.867421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:49:14.346562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:49:21.849882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:49:32.909177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:49:43.316628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:49:50.920705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:50:01.680753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:50:09.072082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:50:16.303586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:50:26.358898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:50:34.790530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-22T18:50:44.530967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-07-22T18:51:10.345330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-22T18:51:10.865283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-22T18:51:11.401234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-22T18:51:11.961183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-07-22T18:51:12.769111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-07-22T18:50:55.186704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-22T18:50:56.546582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexCountryTotal CasesNew CasesTotal DeathsNew DeathsTotal RecoveredNew RecoveredActive CasesSerious/CriticalTotal Cases/1MDeaths/1MTotal TestsTest/1MPopulationContinent
024Philippines15244386549.02687432.014495565341.04800820311372024216141903145274111113614Asia
1179Guinea-Bissau410821.0741.0374419.0290420373778087387192016751Africa
2131Guinea2482358.01951.02371964.0909241838144893653623413505815Africa
3102Ghana99160-1.0815-1.095221-1.031241231232613573894274831753220Africa
4123Trinidad and Tobago36626272.010033.029841178.0578222260827142562691824941404260North America
5119Malawi45465781.0138926.035432229.086442852314713097661576619647757Africa
6198Faeroe Islands9498.01-1.085716.091-11934420351544716588549058Europe
755Bulgaria42331996.0181873.0397831183.073018061411263834695515033256893263Europe
880Armenia227936220.045731.021858556.04778-176770154012823454319002969077Asia
9142Papua New Guinea175249.01921.01717320.01597192021143569157349124865Australia/Oceania

Last rows

df_indexCountryTotal CasesNew CasesTotal DeathsNew DeathsTotal RecoveredNew RecoveredActive CasesSerious/CriticalTotal Cases/1MDeaths/1MTotal TestsTest/1MPopulationContinent
9078Moldova258237141.062323.025112592.08805764173154913598563379304024080Europe
91183Liechtenstein30701.059-1.029951.016280276154349126128457538243Europe
9285Zambia188573971.0316224.0175429701.099827359965167203049910730318922971Africa
9363Dominican Republic338291316.039291.02828412196.05152124630859358180330516450110962301North America
94182Mauritius3120-1.019-1.01854-1.01247-12449153586752815371273991Africa
95189Turks and Caicos2458-1.018-1.02404-1.036-16259745894789241396139267North America
9651Paraguay447146879.01444652.04076221342.02507843361892200016405152270737224621South America
9766Denmark308615851.02542-1.0294559653.011514125308443774244457127706475813680Europe
9830Romania1081875102.0342602.0104695271.0663385663617931028515853842519102321Europe
9990Kyrgyzstan1527091102.022059.01289051213.0215991952300833214636862205276637212Asia